METHOD AND APPARATUS FOR A WIRELESS MOBILE SYSTEM IMPLEMENTING BEAM STEERING PHASE ARRAY ANTENNA

• US 20110260918A1
• Filed: 10/24/2010
• Published: 10/27/2011
• Est. Priority Date: 08/27/2009
• Status: Active Grant

• Current Assignee: TurboCode LLC (Jeffrey M. Gross)
• Original Assignee: ICOMM Technologies, Inc.
• Inventors: NGUYEN, QUANG
• Granted Patent: US 8,468,414 B2
• Time in Patent Office: Days
• US Class Current: 342/368
• CPC Class Codes: H04B 7/0617 for beam forming
• CPC Class Codes: H04B 7/0682 using phase diversity (e.g. phase sweeping)
• CPC Class Codes: H04L 1/005 Iterative decoding, including iteration between signal detection and decoding operation
• CPC Class Codes: H04L 1/0055 MAP-decoding
• CPC Class Codes: H04L 1/006 Trellis-coded modulation
• CPC Class Codes: H04L 1/0071 Use of interleaving interleaving per se H03M13/27
• CPC Class Codes: Y02D 30/70 in wireless communication networks

Abstract

In a preferred embodiment, a wireless mobile system implementing Beam Steering Phased Array Antenna is disclosed having an array of [N×N] antennas mounting on the wireless mobile Terminal transmits unidirectional signals directly to the Base Station as opposed to transmitting multi-directional signals. The wireless mobile system implementing beam-steering phased array antenna, wherein the phases of the signals fed to the array antenna elements are varied by the phase shifters in small programmable step values calculated by the Digital Signal Processor (DSP) Phase Processor which causes the antennas radiation beam to be steered at angle θ. Thus, improvements in beam steering for transmitting unidirectional signals directly to the Base Station will greatly reduce the energy required by the wireless terminal for transmitting signals to the Base Station. And, the effective result would achieve our green-energy technology requirements.

Claims

1. A wireless mobile system with beam-steering phased array antennas, the wireless mobile system comprising:

at least one or plurality of phased array antenna, wherein each phased array antenna comprises an array [N×

N] of radiating elements;

at least one or plurality of DSP Phase Processor for calculating the desired phase shift values to achieve transmit wave lobe steering angle θ

for controlling the phased shifters associated with each radiating elements;

at least one or plurality of Phase Shifter associated with each radiating elements adapted to receive transmit signals, wherein each phase shifter varies the phase of the transmit signals before feeding to the radiating elements;

at least one or plurality of base station tracker associated with each radiating elements adapted to receive signals, wherein each base station tracker determine the current location of the communicating base station;

at least one or plurality of Signal Feed Processor for distributing the transmit signals to each radiating elements;

at least one or plurality of Transmitter adapted to receive OFDM signals for further modulating and wave shaping the signals before sending to the radiating elements;

at least one or plurality of Transmit Baseband Processor adapted to received digital information data for transmitting, wherein each Transmit Base Processor further comprises;

at least one or plurality of inverse Fast Fourier Transform (iFFT) module adapted to receive encoded data sequences for modulating the data into multiple Orthogonal frequency-division multiplexing (OFDM) signals before feeding to the Transmitter module; and

at least one or plurality of Turbo Codes Encoder module adapted to receive data for encoding input information into encoded data sequences with parity bits for error-correction before feeding to the iFFT module.

at least one or plurality of Clock Management Logic for controlling the clock distributions to the target modules; and

at least one or plurality of Power Management Logic for controlling the power distributions to the target modules.

2. A MIMO system with diversity baseband processing for iteratively decoding data received on multiple data paths from at least two or multiple antennas, the MIMO baseband processing system comprising:

at least one each Pre-Processor adapted to receive the data received on corresponding one of the multiple data paths RX(i), wherein each Pre-Processor further comprises;

at least one I/Q Demodulator to demodulated the receive signal into baseband component I and Q signals;

at least one Guard Interval Removal for removing cyclic-prefix;

at least one Clock Recovery circuit to calculate sampling error to control the Digital Phase locked-loop; and

a Digital Phase locked-loop circuit to produce the sampling clock for the I/Q Demodulator circuit;

a Diversity Processor adapted to received signals from the Pre-Processors, wherein the Diversity Processor further comprises;

an Algorithm to select the optimum pair of diversity channels;

a Combiner to combine the signals pair of diversity channels; and

a Matched Filter to produce an output signal of the diversity channels.

at least one or plurality of M-point Complex FFT Processor to process each of the M sub-channels;

a MUX to convert the data from each channel into a serial stream of data; and

at least one or plurality of Turbo Codes Decoder to process the data stream from each channel to produce a error-free data output, wherein each Turbo Codes Decoder further comprises;

at least two soft decision decoders adapted to receive soft data associated with corresponding signal paths, wherein the at least two soft decision decoders are serially coupled and have at least a first soft decision decoder and a last soft decision decoder, wherein the last soft decision decoder is adapted to output soft data for the serially coupled series of soft decision decoders, wherein each soft decision decoder further comprises;

a branch metric module that is adapted to receive soft input signal and is configured to compute branch metric values for each branch in a Trellis by calculating a Euclidean distance for each branch;

a branch metric memory module that is coupled to the branch metric module and is adapted to store data associated at least with the branch metric values;

a state metric module that is coupled to the branch metric memory module and is configured to compute state metric values for each state in the Trellis using the computed branch metric values;

an add-compare-select circuit that is coupled to the state metric module and is configured to compute state metric values at each node in the Trellis;

a state metric memory module that is coupled to the state metric module and is adapted to store data associated at least with the state metric values; and

a logarithmic likelihood ratio (LLR) function computation module that is coupled to at least the branch metric memory module and the state metric memory module, wherein the computation module is configured to compute a soft decision output based at least on the branch metric values and the state metric values.

3. The soft decision decoder according to claim 2 decoding a plurality of diversity baseband signals, wherein the computation module is implemented with a logarithm approximation algorithm.

4. The soft decision decoder according to claim 2 decoding a plurality of diversity baseband signals, wherein the add-compare-select circuit comprises:

a first adder for computing the sum of a first state metric value and a first branch metric value;

a second adder for computing the sum of a second state metric value and a second branch metric value;

a comparator for comparing the results of the first adder and the results of the second adder; and

a multiplexer for selecting a larger sum for a predetermined state.

5. A soft decision decoder according to claim 2 decoding a plurality of diversity baseband signals comprising:

branch metric means for receiving soft input signal and computing branch metric values for each branch in a Trellis by calculating a Euclidean distance for each branch;

branch metric memory means for storing data associated at least with the branch metric values;

state metric means for computing state metric values for each state in the Trellis using the computed branch metric values;

add-compare-select means for computing state metric values at each node in the Trellis;

state metric memory means for storing data associated at least with the state metric values;

logarithmic likelihood ratio (LLR) function computation means for computing a soft decision output based at least on the branch metric values and the state metric values; and

control logic state machine means for controlling operations of at least one of the branch metric means, the branch metric memory means, the state metric means, the add-compare-select means, the state metric memory means, and the computation means.

6. A method of implementing a wireless mobile system with beam-steering phased array antennas, comprising:

calculating the current location of the base station relative to the mobile device;

calculating the required phase to control the phase shifters;

receiving transmit signals from the signal feeder;

varying the phase of transmit signals through the phase shifter;

feeding the transmit signals from the phase shifter to the radiation elements;

transmitting the phased steering transmit waves to the base station;

7. A method of implementing a MIMO system with diversity baseband processing, comprising:

receiving multipath signals from the at least a pair of diversity antennas;

pre-processing multipath signals using the at least a plurality of I/Q demodulators;

processing demodulated I/Q signals through a diversity processor;

processing demodulated I/Q signals using the at least a plurality of invert Fast Fourier Transform (iFFT) processor;

processing demodulated baseband signals using the at least a plurality of Turbo Codes Decoders; and

hard decoding the output data.

8. A method of implementing a soft decision decoder for decoding a plurality of diversity baseband signals from a plurality of diversity antennas, comprising:

receiving diversity soft decision data;

utilizing a sliding window of a predetermined block size to process the soft decision data;

computing a branch metric for each data element of the soft decision data associated with the predetermined block size, wherein the branch metric is computed for branches entering each state in the Trellis;

computing a forward recursion state metric for each data element of the soft decision data associated with the predetermined block size, wherein the state metric is computed for each state in the Trellis;

computing a backward recursion state metric for each data element of the son decision data associated with the predetermined block size, wherein the state metric is computed for each state in the Trellis;

computing logarithm maximum a posteriori probability values based on at least the branch metric, the forward recursion state metric, and the backward recursion state metric for each data element of the soft decision data associated with the predetermined block size; and

providing soft-decisions based on the logarithm maximum a posteriori probability values

wherein computing the branch metric for each data element comprises calculating a Euclidean distance for each branch.

9. The method according claim 8 decoding a plurality of diversity baseband signals, wherein performing, for a predetermined number of times, iterative decoding at the first and second decoders comprises:

processing the soft decision data using an algorithm comprised of a log-MAP algorithm or a logarithm approximation algorithm to generate a soft decision;

storing the soft decision;

performing iterative decoding from the first soft decision decoder to the last soft decision decoder in circular circuit for the predetermined number of times;

determining a hard decision output.

10. A method of processing according claim 8 decoding a plurality of diversity baseband signals, wherein the data is received from a plurality of diversity antennas,

wherein performing, for a predetermined number of times, iterative decoding at the first and second decoders comprises:

processing the data received on the diversity paths using an algorithm comprised of a log-MAP algorithm or a logarithm approximation algorithm to generate a soft decision;

storing the soft decision;

performing iterative decoding from the first soft decision decoder to the last soft decision decoder in circular circuit for the predetermined number of times;

determining a hard decision output.

11. The method according to claim 8 decoding a plurality of diversity baseband signals, wherein the data is received on the multiple data paths from a plurality of diversity antennas, wherein utilizing a sliding window having a predetermined block size to process data received at the first decoder comprises utilizing a sliding window having a predetermined block size for pipelined processing of soft decision data from the first soft decision decoder to the second soft decision decoder iteratively.